Physical Sciences and Mathematics Commons^™

Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele

Research Collection School Of Computing and Information Systems

There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to …

Feature Pyramid Transformer, Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Feature interactions across space and scales underpin modern visual recognition systems because they introduce beneficial visual contexts. Conventionally, spatial contexts are passively hidden in the CNN’s increasing receptive fields or actively encoded by non-local convolution. Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales. To this end, we propose a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT). It transforms any feature pyramid into another feature pyramid of the same size but with richer contexts, by using …

Creativity And Engagement In Ideas Crowdsourcing: A Situation Awareness Perspective, James Gitau Wairimu

Theses and Dissertations

This dissertation investigates the influence of performance feedback in user motivation and creativity development in idea crowdsourcing engagement. Creativity occurs when users of idea crowdsourcing communities engage in direct and indirect interactions that expose them to a pool of knowledge that enhances their cognitive development leading to the contribution of novel ideas for innovation in organizations. Additionally, participant motivation to engage in ideas crowdsourcing is increased through rewards and conditions that make the ideation process more inclusive and enjoyable. An idea network design is developed by applying social network analysis principles. The idea network design consists of mechanisms for motivating …

A Gis-Based Method For Archival And Visualization Of Microstructural Data From Drill Core Samples., Elliott Holmes

Electronic Theses and Dissertations

Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and explore the available microstructural data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed procedures for: 1. A GIS-based approach for spatially referenced visualization and storage of microstructural data from drill core billet samples; and 2. Producing 3D models of …

Colleague To Banner Migration: Data Conversion Guide For Institutional Research, Laura Osborn

Masters Theses & Doctoral Dissertations

When the SDBOR decided to migrate their current student information system into a shared system with HR and Finance, adjustments needed to be made to accommodate for current Banner settings and work around tables that were already populated with HRFIS data. The change in data type of the student identifier from that of a 7-digit numeric field to a 9- digit alpha-numeric field poses problems for running aggregate data calculations. Additional complications include having some information such as first-generation status that was not migrated between the systems, and cases such as college coding where tables that were designed for student …

Diffusion Of Falsehoods On Social Media, Kelvin Kizito King

Theses and Dissertations

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher …

Maia And Admonita: Mandatory Integrity Control Language And Dynamic Trust Framework For Arbitrary Structured Data, Wassnaa Al-Mawee

Dissertations

The expansion of attacks against information systems of companies that operate nuclear power stations and other energy facilities in the United States and other countries, are noticeable with potential catastrophic real-world implications. Data integrity is a fundamental component of information security. It refers to the accuracy and the trustworthiness of data or resources. Data integrity within information systems becomes an important factor of security protection as the data becomes more integrated and crucial to decision-making. The security threats brought by human errors whether, malicious or unintentional, such as viruses, hacking, and many other cybersecurity threats, are dangerous and require mandatory …

Learning Transferrable Parameters For Long-Tailed Sequential User Behavior Modeling, Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance of sequential modeling heavily depends on the scale and quality of historical behaviors. However, the number of user behaviors inherently follows a long-tailed distribution, which has been seldom explored. In this work, we argue that focusing on tail users could bring more benefits and address the long tails issue by learning transferrable parameters from both optimization and feature perspectives. Specifically, we propose a gradient alignment optimizer and adopt an adversarial training scheme to facilitate knowledge transfer …

A Unified Framework For Sparse Online Learning, Peilin Zhao, Dayong Wong, Pengcheng Wu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online …

Aim 2020 Challenge On Video Extreme Super-Resolution: Methods And Results, D. Fuoli, Zhiwu Huang, S. Gu, R. Timofte, A. Raventos, A. Esfandiari, S. Karout, X. Xu, X. Li, X. Xiong, J. Wang, Michelini P. Navarrete, W. Zhang, D. Zhang, H. Zhu, D. Xia, H. Chen, J. Gu, Z. Zhang, T. Zhao

Research Collection School Of Computing and Information Systems

This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the lowresolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. …

Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers, Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao

Research Collection School Of Computing and Information Systems

Motivation: Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture …

Meta-Learning On Heterogeneous Information Networks For Cold-Start Recommendation, Yuanfu Lu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Cold-start recommendation has been a challenging problem due to sparse user-item interactions for new users or items. Existing efforts have alleviated the cold-start issue to some extent, most of which approach the problem at the data level. Earlier methods often incorporate auxiliary data as user or item features, while more recent methods leverage heterogeneous information networks (HIN) to capture richer semantics via higher-order graph structures. On the other hand, recent meta-learning paradigm sheds light on addressing cold-start recommendation at the model level, given its ability to rapidly adapt to new tasks with scarce labeled data, or in the context of …

Social Participation Performance Of Wheelchair Users Using Clustering And Geolocational Sensor's Data, Yukun Yin, Kar Way Tan

Research Collection School Of Computing and Information Systems

For wheelchair users, social participation and physical mobility play a significant part in determining their mental health and quality of life outcomes. However, little is known about how wheelchair users move about and engage in social interactions within their life-spaces. In this project, we investigate the social participation performance of the wheelchair users based on a combination of geolocational and lifestyle survey data collected over a period of three months. This paper adopts a multi-variate approach combining geolocational travel patterns and various factors such as independence, willingness and self-perception to provide multi-faceted analysis to their lifestyles. We provide profiles of …

An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …

Ums Data Governance Charter & Framework, University Of Maine System Data Governance Council

General University of Maine Publications

The University of Maine System Data Governance program launched in 2017, in order to emphasize the importance of data integrity, and to formalize processes related to data management and usage. The program embraces the following vision:

Data Governance Vision: Data of the University of Maine System (UMS) are system-wide institutional assets that are leveraged to foster a culture of data-informed decisions to benefit all UMS institutions and stakeholders. This vision can be achieved through successful implementation of the following mission:

Data Governance Mission: UMS Data Governance promotes data stewardship and communication to ensure that valid and reliable data are protected …

Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct fewshot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. …

A Generalised Bound For The Wiener Attack On Rsa, Willy Susilo, Joseph Tonien, Guomin Yang

Research Collection School Of Computing and Information Systems

Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N, it has been a general belief that the Wiener attack works for d

A Fast Anderson-Chebyshev Acceleration For Nonlinear Optimization, Zhize Li, Jian Li

Research Collection School Of Computing and Information Systems

Anderson acceleration (or Anderson mixing) is an efficient acceleration method for fixed point iterations $x_{t+1}=G(x_t)$, e.g., gradient descent can be viewed as iteratively applying the operation $G(x) \triangleq x-\alpha\nabla f(x)$. It is known that Anderson acceleration is quite efficient in practice and can be viewed as an extension of Krylov subspace methods for nonlinear problems. In this paper, we show that Anderson acceleration with Chebyshev polynomial can achieve the optimal convergence rate $O(\sqrt{\kappa}\ln\frac{1}{\epsilon})$, which improves the previous result $O(\kappa\ln\frac{1}{\epsilon})$ provided by (Toth and Kelley, 2015) for quadratic functions. Moreover, we provide a convergence analysis for minimizing general nonlinear problems. Besides, …

A Systematic Density-Based Clustering Method Using Anchor Points, Yizhang Wang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to …

Bootstrapping Web Archive Collections From Micro-Collections In Social Media, Alexander C. Nwala

Computer Science Theses & Dissertations

In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. …

Accelerating Exact Constrained Shortest Paths On Gpus, Shengliang Lu, Bingsheng He, Yuchen Li, Hao Fu

Research Collection School Of Computing and Information Systems

The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and …

An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. “Epoch-wise'' means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ”Empirical'' means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent …

Love A Restaurant? Swipe Right On Foodrecce, Hady W. Lauw, Smu Office Of Research

Research@SMU Infographics

A bunch of your friends wants to meet for dinner, but nobody can agree on where and what to eat? FoodRecce can help! FoodRecce is an app, developed under the Preferred.AI initiative, that provides recommendations on restaurants based on users' locations and past preferences.

Specialization: Do Your Job Well Helping Students Who Are Considering A Career In Programming Know How To Invest Their Time., Scott Pulley

Marriott Student Review

The article examines the effects of specialization on the hiring process for undergraduates studying programming whether in information systems or computer science.

Visual Analytics Of Electronic Health Records With A Focus On Acute Kidney Injury, Sheikh S. Abdullah

Electronic Thesis and Dissertation Repository

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports …

Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College, Mohammed Ali

Technology Faculty Publications and Presentations

A novel technique was applied to a college student database to identify the cognitive and non-cognitive factors that predict college students’ course withdrawal behaviors. Predictors such as high school grade point average (HSGPA), standardized test scores (ACT–American College Test or SAT-Scholastic Aptitude Test), number of credit hours enrolled, and age were analyzed in this study. Data mining software algorithms were used to study information about undergraduate students at a west-south-central state university in the United States. The study results revealed that two factors, number of enrolled credit hours, and a student’s age have the most effect on collegiate course withdrawal …

Lulling Waters: A Poetry Reading For Real-Time Music Generation Through Emotion Mapping, Ashley Muniz, Toshihisa Tsuruoka

Electronic Literature Organization Conference 2020

Through a poetic narrative, “Lulling Waters” tells the story of a whale overcoming the loss of his mother, who passed away from ingesting plastic, as he attempts to escape from the polluted oceanic world. The live performance of this poem utilizes a software system called Soundwriter, which was developed with the goal of enriching the oral storytelling experience through music. This video demonstrates how Soundwriter’s real-time hybrid system was able to analyze “Lulling Waters” through its lexical and auditory features. Emotionally salient words were given ratings based on arousal, valence, and dominance while the emotionally charged prosodic features of the …

Patterns Of Population Displacement During Mega-Fires In California Detected Using Facebook Disaster Maps, Shenyue Jia, Seung Hee Kim, Son V. Nghiem, Paul Doherty, Menas Kafatos

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented …

Acceleration For Compressed Gradient Descent In Distributed And Federated Optimization, Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik

Research Collection School Of Computing and Information Systems

Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While in other contexts the best performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of iterations, there are no methods which combine the benefits of both gradient compression and acceleration. In this paper, we remedy this situation and propose the first accelerated compressed gradient descent (ACGD) methods. In the single machine regime, we prove that ACGD enjoys the rate $O\Big((1+\omega)\sqrt{\frac{L}{\mu}}\log \frac{1}{\epsilon}\Big)$ for $\mu$-strongly convex problems and $O\Big((1+\omega)\sqrt{\frac{L}{\epsilon}}\Big)$ for convex problems, respectively, …

Big Data, Spatial Optimization, And Planning, Kai Cao, Wenwen Li, Richard Church

Research Collection School Of Computing and Information Systems

Spatial optimization represents a set of powerful spatial analysis techniques that can be used to identify optimal solution(s) and even generate a large number of competitive alternatives. The formulation of such problems involves maximizing or minimizing one or more objectives while satisfying a number of constraints. Solution techniques range from exact models solved with such approaches as linear programming and integer programming, or heuristic algorithms, i.e. Tabu Search, Simulated Annealing, and Genetic Algorithms. Spatial optimization techniques have been utilized in numerous planning applications, such as location-allocation modeling/site selection, land use planning, school districting, regionalization, routing, and urban design. These methods …